phData

FEBRUARY 21, 2025

In this post, we will be particularly interested in the impact that cloud computing left on the modern data warehouse. We will explore the different options for data warehousing and how you can leverage this information to make the right decisions for your organization. Understanding the Basics What is a Data Warehouse?

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Pickl AI

NOVEMBER 15, 2023

Discover the nuanced dissimilarities between Data Lakes and Data Warehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and Data Warehouses. It acts as a repository for storing all the data.

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Smart Data Collective

APRIL 29, 2020

Extraction, Transform, Load (ETL). The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated. Data analytics and visualisation.

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Flipboard

DECEMBER 11, 2024

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Big Data Architect. Zach Mitchell is a Sr.

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ODSC - Open Data Science

JUNE 12, 2023

They all agree that a Datamart is a subject-oriented subset of a data warehouse focusing on a particular business unit, department, subject area, or business functionality. The Datamart’s data is usually stored in databases containing a moving frame required for data analysis, not the full history of data.

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Women in Big Data

OCTOBER 9, 2024

A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake. Once ingested, the data is prepared through filtering, error correction, and restructuring for ease of use.

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phData

APRIL 28, 2025

A Matillion pipeline is a collection of jobs that extract, load, and transform (ETL/ELT) data from various sources into a target system, such as a cloud data warehouse like Snowflake. The workflow well reference throughout this blog was built using customer data from TrellisMart, a fictional retail company.

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phData

APRIL 20, 2023

In today’s world, data-driven applications demand more flexibility, scalability, and auditability, which traditional data warehouses and modeling approaches lack. This is where the Snowflake Data Cloud and data vault modeling comes in handy. What is Data Vault Modeling?

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How to Learn Machine Learning

APRIL 26, 2025

This includes duplicate removal, missing value treatment, variable transformation, and normalization of data. Tools like Python (with pandas and NumPy), R, and ETL platforms like Apache NiFi or Talend are used for data preparation before analysis.

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AWS Machine Learning Blog

JUNE 25, 2024

The raw data is processed by an LLM using a preconfigured user prompt. The processed output is stored in a database or data warehouse, such as Amazon Relational Database Service (Amazon RDS). The stored data is visualized in a BI dashboard using QuickSight. The LLM generates output based on the user prompt.

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Dataconomy

MARCH 24, 2023

BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions. A career path in BI can be a lucrative and rewarding choice for those with interest in data analysis and problem-solving. How to become a blockchain maestro?

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Dataconomy

MARCH 24, 2023

BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions. A career path in BI can be a lucrative and rewarding choice for those with interest in data analysis and problem-solving. How to become a blockchain maestro?

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

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phData

SEPTEMBER 19, 2023

A rigid data model such as Kimball or Data Vault would ruin this flexibility and essentially transform your data lake into a data warehouse. However, some flexible data modeling techniques can be used to allow for some organization while maintaining the ease of new data additions.

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Pickl AI

MARCH 3, 2025

Here’s a glimpse into their typical activities Data Acquisition and Cleansing Collecting data from diverse sources, including databases, spreadsheets, and cloud platforms. Ensuring data accuracy and consistency through cleansing and validation processes. Developing data models to support analysis and reporting.

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Pickl AI

APRIL 26, 2024

Top 50+ Interview Questions for Data Analysts Technical Questions SQL Queries What is SQL, and why is it necessary for data analysis? SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. Explain the Extract, Transform, Load (ETL) process.

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Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

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Pickl AI

JANUARY 28, 2025

Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format. Data Quality Management : Ensures that the integrated data is accurate, consistent, and reliable for analysis. This can involve: Data Warehouses: These are optimized for query performance and reporting.

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Pickl AI

JANUARY 15, 2025

The primary functions of BI tools include: Data Collection: Gathering data from multiple sources including internal databases, external APIs, and cloud services. Data Processing: Cleaning and organizing data for analysis. Data Analysis : Utilizing statistical methods and algorithms to identify trends and patterns.

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Pickl AI

JULY 6, 2023

Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis. It applies the data structure during querying rather than data ingestion. This delay makes Hive less suitable for real-time or interactive data analysis. Why Do We Need Hadoop Hive?

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Pickl AI

JULY 25, 2024

In this blog, we’ll delve into the intricacies of data ingestion, exploring its challenges, best practices, and the tools that can help you harness the full potential of your data. Batch Processing In this method, data is collected over a period and then processed in groups or batches. The post What is Data Ingestion?

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Pickl AI

AUGUST 9, 2024

Improved Data Navigation Hierarchies provide a clear structure for users to navigate through data. Enhanced Data Analysis By allowing users to drill down into data, hierarchies enable more detailed analysis. Retail Industry In a retail data warehouse , hierarchies can be used to organise product categories.

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phData

JANUARY 4, 2023

Why You Should Consider Migrating from Netezza to Snowflake With Netezza, IBM was one of the first companies to provide a data warehousing solution to allow organizations to analyze and manage large amounts of data. However, as technology has evolved, the need for more advanced, agile data warehousing solutions has become apparent.

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ODSC - Open Data Science

JANUARY 18, 2024

Like with any professional shift, it’s always good practice to take inventory of your existing data science strengths. Data scientists typically have strong skills in areas such as Python, R, statistics, machine learning, and data analysis. With that said, each skill may be used in a different manner.

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Pickl AI

JULY 3, 2023

Here are steps you can follow to pursue a career as a BI Developer: Acquire a solid foundation in data and analytics: Start by building a strong understanding of data concepts, relational databases, SQL (Structured Query Language), and data modeling.

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phData

JANUARY 5, 2023

Python can be used to migrate your data from a previous platform to Snowflake , create or manage data pipelines for Extract, Transform, and Load (ETL) processes, perform data science tasks such as machine learning or create data analysis visualizations.

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phData

JULY 10, 2024

Unlike traditional BI tools, its user-friendly interface ensures that users of all technical levels can seamlessly interact with data. The platform’s integration with cloud data warehouses like Snowflake AI Data Cloud , Google BigQuery, and Amazon Redshift makes it a vital tool for organizations harnessing big data.

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IBM Journey to AI blog

AUGUST 4, 2023

Data fabric Data fabric architectures are designed to connect data platforms with the applications where users interact with information for simplified data access in an organization and self-service data consumption. Then, it applies these insights to automate and orchestrate the data lifecycle.

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Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

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Alation

FEBRUARY 20, 2020

Data lakes, while useful in helping you to capture all of your data, are only the first step in extracting the value of that data.

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ODSC - Open Data Science

APRIL 4, 2024

ODSC Highlights Announcing the Keynote and Featured Speakers for ODSC East 2024 The keynotes and featured speakers for ODSC East 2024 have won numerous awards, authored books and widely cited papers, and shaped the future of data science and AI with their research. Learn more about them here!

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AWS Machine Learning Blog

APRIL 16, 2024

The output of a query can be displayed directly within the notebook, facilitating seamless integration of SQL and Python workflows in your data analysis. These connections are used by AWS Glue crawlers, jobs, and development endpoints to access various types of data stores. They can also be written to a pandas DataFrame.

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DagsHub

NOVEMBER 11, 2024

Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data. These capture the semantic relationships between words, facilitating tasks like classification and clustering within ETL pipelines. Multimodal embeddings help combine unstructured data from various sources in data warehouses and ETL pipelines.

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The MLOps Blog

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. I’ll show you best practices for using Jupyter Notebooks for exploratory data analysis. When data science was sexy , notebooks weren’t a thing yet.

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Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. The existing Data Catalog becomes the Default catalog (identified by the AWS account number) and is readily available in SageMaker Lakehouse.

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Pickl AI

MARCH 19, 2025

It helps data engineers collect, store, and process streams of records in a fault-tolerant way, making it crucial for building reliable data pipelines. Amazon Redshift Amazon Redshift is a cloud-based data warehouse that enables fast query execution for large datasets.

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